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ME_Thesis_1.pptx
1. Dissertation Phase – II
Presentation on
PARAMETRIC OPTIMIZATION OF STRAIN GAUGE LOAD
CELL
Presented By
Mr. Rakesh Ramchandra Kolhapure
Submitted for the degree of
M.E. Mechanical (Product Design and Development)
under the Guidance of
Prof.(Dr.) V. D. Shinde
Department of Mechanical Engineering
D.K.T.E.’S TEXTILE & ENGINEERING INSTITUTE,
ICHALKARANJI
1
2. Contents
• Introduction
• Literature Review
• Problem Definition
• Objectives
• Research Approach (Methodology)
• Design of Experiment (DOE)
• Structural Analysis (FEA)
• Multi-Objective Optimization (Grey Relational Analysis)
• Validation using Photoelastic Model (ESA)
• Conclusions
• References
2
3. Introduction
Strain gauge load cell is a force transducer that is used to convert a force into electrical signal
• Shear Beam Load cell
• S Load cell
• Column Beam Load cell
Good resistance against side loads and better overload
capabilities
Use in Tensile and Compression.
Non linear due to change in cross section
Types of Strain Gauge Load Cells
3
5. Literature Survey
Sr. No. Author and year Type of load
cell
Input Parameters Output Parameters Method used Remarks
1 Soni and Priyadarshni (2010) ‘Parallelogram’ 1. Cavity Length
2. Cavity Height
3. Radius
1. Sensitivity FEA Optimization of load
cell used in aerodynamic
field to with stand side
load acting on it.
2 Pacnik and Novak (2010) ‘Hydraulic’ 1. Temperature
2. Load
3. Pressure
1. Sensitivity
2. Low hysteresis
FEA Design of small load
cell used in kitchen
appliance.
3 Thakkar et. al (2013) ‘Beam’ 1. Load 1. Stress FEA Improving performance
(Life) of load cell used
for weighing
application.
4 Thein (2013) ‘S’ 1. Height
2. Width
3. Slot Thickness
1. Stress
2. Reliability
loading case-
index
FEA
Shape optimization
5 Drout and Champoux (2014) ‘Beam’
- - Solid Works
simulation
Analysis of beam type
load cell under dynamic
condition used in
cycling.
5
6. Literature Survey (… continued)
Sr. No. Author and year Type of load cell Input Parameters Output Parameters Method used Remarks
6 Bethe (1994) ‘Circular plate force’ 1. Diameter of
circular plate
2. Width
3. Height
1. Weight
2. Improved
linearity
Numerical
FEM
calculation
Optimization of
compact force-
sensor/load-cell family
7 Liu et. al (2006) ‘Beam’ 1. Dimensional
parameter
1. Sensitivity FEM Development of a
wearable force sensor
system for human
dynamics analysis in
biomedical application
8 Equbal et. al (2012)
- 1. Flash thickness
2. Flash Width
3. Corner radii
4. Fillet radii
1. Minimize
forging load
FEM and
Taguchi
Shape optimization of
connecting rod
Stefanescu D. and Stefanescu A. (2001)
Study the various parameters (mass, sensitivity and load ) while selecting force transducers.
6
7. Summery of Literature
• Literature provides information about various types of load cells used in various application.
• After reviewing the literature cited above, it can be summarized that design factors such as geometry,
material will influence the performance measures like sensitivity, volume, deformation.The literature
survey has revealed that a little research has been conducted to obtain the optimal levels of design
parameters which yield the best quality of load cell.
7
8. Problem Definition
Review of literature cited “PARAMETRIC OPTIMIZATION OF STRAIN GAUGE LOAD CELL” goal
has been undertaken for current research work.
8
9. Objectives
To achieve research goal following objectives are considered,
1. Study of different types of load cells used for weighing application.
2. Analysis of functional parameters of load cells.
3. Choosing critical parameters of load cells.
4. DOE for conducting simulation runs with selected parameters.
5. Modeling and structural analysis of load cells.
6. Multi-objective optimization of the critical parameters of load cells using grey relational analysis.
7. Validation of the results by conducting confirmation experiments using photo elastic model on polariscope.
9
10. Research Approach (Methodology)
Strain Gauges Load Cells
S type
Double Ended Shear
Beam type
Washer type
FEA
Parameter Selection
Verification
Optimization
Performance
Measure
Parameter
Optimization
Photo elastic model
Polariscope
Stress Analysis
Validation
10
11. Design of Experiment (DOE)
• ‘S’ type load cell
Source: ADI ARTECH TRANSDUCERS PVT.
LTD, VADODARA
Assumptions:
• Bottom surface of load cell is fixed.
• Pressure is applied on top surface of load cell.
• Ø16.5 mm is fixed inside of which strain gauges are fitted.
• The 12.5 mm thickness is not changed due to M6X1 tapping
provided for connecting adaptor.
Material Young's Modulus Poisson's Ratio Density
EN 24 Steel 2.1 x 105MPa 0.3 7840 Kg/m3
Capacity – 20 to 100 Kgf
11
12. Design of Experiment (… continued)
Sr.
No.
Parameters Unit Level 1 Level 2 Level 3
1 Thickness (A) mm 10.4 13 15.6
2 Length (B) mm 40 50 60
3 Height (C) mm 19.2 24 28.8
Parameters and level for ‘S’ type load cell
OA Parameter 1 Parameter 2 Parameter 3
1 10.4 40 19.2
2 10.4 50 24
3 10.4 60 28.8
4 13 40 24
5 13 50 28.8
6 13 60 19.2
7 15.6 40 28.8
8 15.6 50 19.2
9 15.6 60 24
L9 Design matrix for ‘S’ type load cell
Selection of Orthogonal Array
(DOF)R=P×(L-1)
Where,(DOF)R=Degree of freedom of Expt.
P=No. of parameters , L=No. of levels
(DOF)R=3×(3-1)= 6
“DOF of the OA should be greater than or equal to the
total DOF required for the experiment”
Here, DOF of OA=DOF of Expt.
Therefore L9 OA is selected 12
13. Design of Experiment (… continued)
• ‘Washer’ type load cell
Assumptions:
• Bottom surface of load cell is fixed.
• Pressure is applied on top surface of load cell.
Source: ADI ARTECH TRANSDUCERS PVT.
LTD, VADODARA
Material Young's Modulus Poisson's Ratio Density
Stainless Steel 1.9x 105MPa 0.31 7840 Kg/m3
Capacity – 5 Tf
13
14. Design of Experiment (… continued)
Expt. No. Parameter 1 Parameter 2
1 40 64
2 40 80
3 40 96
4 50 64
5 50 80
6 50 96
7 60 64
8 60 80
9 60 96
L9 Design Matrix of ‘Washer’ load cell
Sr. No. Parameters Unit Level 1 Level 2 Level 3
1 Height (A) mm 40 50 60
2 Outer Diameter (B) mm 64 80 96
Parameter and level for ‘Washer’ type load cell
Orthogonal array is selected as per above
mentioned procedure
14
15. Design of Experiment (… continued)
• ‘Double Ended Shear Beam’ type load cell
Assumptions:
• It is fixed at both ends.
• Pressure is applied to the center of load cell.
Source: ADI ARTECH TRANSDUCERS PVT.
LTD, VADODARA
Same Material used for ‘S’ and ‘Double
Ended Shear Beam’ load cell.
Capacity – 5 Tf
15
16. Design of Experiment (… continued)
Sr. No. Parameters Unit Level 1 Level 2 Level 3
1 Length (A) mm 137.90 197 256.10
2 Height (B) mm 35 50 65
3 Thickness (C) mm 30.10 43 55.90
OA Parameter 1 Parameter 2 Parameter 3
1 137.90 35 30.10
2 137.90 50 43
3 137.90 65 55.90
4 197 35 43
5 197 50 55.90
6 197 65 30.10
7 256.10 35 55.90
8 256.10 50 30.10
9 256.10 65 43
L9 Design Matrix of ‘Double Ended Shear Beam’ load cell
Parameter and level for ‘Double Ended Shear Beam’ type load cell
Orthogonal array is selected as per above
mentioned procedure
16
17. Structural Analysis (FEA)
• Steps in FEM
Import 3D Model
Assign Material Properties
Discretization (Meshing)
Specify the Restraints and Loads
Run Simulation
Visualization of Results
Analyze the Results
Pre-processing
Processing
Post- processing
17
18. Structural Analysis (… continued)
3D model Meshed model Fixed support Pressure (4 N/mm²)
• ‘S’ type load cell
18
19. Structural Analysis (… continued)
Strain distribution of original load cell
• ‘S’ type load cell
19
20. Structural Analysis (… continued)
• ‘Washer’ type load cell
3D model Fixed support Pressure (25.68 N/mm² )
20
Meshed model
21. Structural Analysis (… continued)
• ‘Washer’ type load cell
Strain distribution of original load cell
21
22. Structural Analysis (… continued)
3D model Meshed model
Fixed support
Pressure (131 N/mm2)
• ‘Double Ended Shear Beam’ type load cell
22
23. Structural Analysis (… continued)
Strain distribution of original load cell
• ‘Double Ended Shear Beam’ type load cell
23
24. Multi objective Optimization (GRA)
• Purpose of Grey Relational Analysis (GRA)
Multi-
objective
optimization
Single
objective
optimization
GRA
Grey Relational Analysis (GRA)
• In real world problems, the situation can never be perfectly black (with no information) or perfectly white
(with complete information).
• Situations between these extremes are described as Grey therefore, a Grey system means, a system in which a
part of information is known and a part of information is unknown.
• GRA method effectively used for solving the complicated interrelationships among the designated
performance characteristics. Through this analysis, the “Grey relational Grade” is defined as an indicator of
Multi- objective characteristics for evaluation.
24
25. Multi objective Optimization (… continued)
Normalize the
experimental results of
each performance
characteristics (Grey
relational generating)
Determine the
values of deviation
sequence
Calculate the Grey
Relational
Coefficient
Calculate the Grey
Relational Grade
Establish response
table and response
graph for each level
of process
parameters
Select the optimal levels
of process parameters
Prediction of Grey
Relational Grade for
optimal process
parameters
Steps of Grey Relational Analysis (GRA)
25
26. Multi objective Optimization (… continued)
1. Effect of parameters on volume
Expt.
No.
Volume (mm3)
Average
Volume
S/N
Ratio
(Smaller
is better)
Mean
1 2 3
1 22403.688 22403.688 22403.688 22403.688 -87.006 22403.688
2 26907.002 26907.002 26907.002 26907.002 -88.597 26907.002
3 32641.218 32641.218 32641.218 32641.218 -90.275 32641.218
4 24633.352 24633.352 24633.352 24633.352 -87.830 24633.352
5 29611.568 29611.568 29611.568 29611.568 -89.429 29611.568
6 27661.988 27661.988 27661.988 27661.988 -88.838 27661.988
7 26581.918 26581.918 26581.918 26581.918 -88.492 26581.918
8 26144.338 26144.338 26144.338 26144.338 -88.348 26144.338
9 30935.652 30935.652 30935.652 30935.652 -89.809 30935.652
n = -10 log10 [mean of sum of
squares of measured data]
S/N Ratio: Lower is better
Example,
n = -10 log10 (24633.352²)
= -87.830
Volume analysis of ‘S’ type load cell
• ‘S’ type load cell
26
27. Multi objective Optimization (… continued)
Levels Thickness
(mm)
Length
(mm)
Height
(mm)
1 27317.303 24539.653 25403.338
2 27302.303 27554.303 27492.002
3 27887.303 30543.628 29611.568
Max 27887.303 30543.628 29611.568
Min 27302.303 24539.653 25403.338
Delta 585 6003.975 4208.230
Rank 3 1 2
Mean volume response table of ‘S’ type load cell
15.6
13.0
10.4
30000
28500
27000
25500
24000
60
50
40
28.8
24.0
19.2
30000
28500
27000
25500
24000
Thickness
Mean
of
Means
Length
Height
Main Effects Plot for Means
Data Means
Effects of process parameters on volume
From above graph it is clear that volume increase with increase
in dimension of thickness, length and height of ‘S’ type load cell
Rank shows that length of ‘S’ type load cell is
significant parameter
27
28. Multi objective Optimization (… continued)
Parameter DOF Seq SS Adj SS Adj MS F P % Contribution
Thickness 2 667350 667350 333675 0.700 0.589 0.835
Length 2 51755647 51755647 25877823 54.140 0.018 64.740
Height 2 26564276 26564276 13282138 27.790 0.035 33.220
Error 2 955872 955872 477936 - - 1.190
Total 8 79943145 - - - - -
ANOVA of ‘S’ type load cell for volume
% contribution shows that length of ‘S’ type load cell is significant parameter
28
29. Multi objective Optimization (… continued)
Expt.
No.
Sensitivity
(µstrain/N) Average
sensitivity
S/N
Ratio
(Larger
is
better)
Mean
1 2 3
1 0.181 0.181 0.181 0.181 -14.854 0.181
2 0.164 0.164 0.164 0.164 -15.688 0.164
3 0.100 0.100 0.100 0.100 -20.034 0.100
4 0.035 0.035 0.035 0.035 -29.201 0.035
5 0.093 0.093 0.093 0.093 -20.603 0.093
6 0.289 0.289 0.289 0.289 -10.772 0.289
7 0.010 0.010 0.010 0.010 -40.047 0.010
8 0.180 0.180 0.180 0.180 -14.900 0.180
9 0.150 0.150 0.150 0.150 -16.494 0.150
Sensitivity analysis of ‘S’ type load cell
S/N Ratio: Larger is better
n = -10 log10 [mean of sum squares
of reciprocal of measured data]
Example,
n = -10 log10(1/0.181²)
= -14.854
2. Effect of parameters on sensitivity
29
30. Multi objective Optimization (… continued)
Levels Thickness
(mm)
Length
(mm)
Height
(mm)
1 0.148 0.075 0.217
2 0.139 0.146 0.116
3 0.113 0.180 0.068
Max 0.148 0.180 0.217
Min 0.113 0.075 0.068
Delta 0.035 0.105 0.149
Rank 3 2 1
Mean sensitivity response table of ‘S’ type load cell
15.6
13.0
10.4
0.20
0.15
0.10
0.05
60
50
40
28.8
24.0
19.2
0.20
0.15
0.10
0.05
Thickness
Mean
of
Means
Length
Height
Main Effects Plot for Means
Data Means
Effects of process parameters on sensitivity
Rank shows that height of ‘S’ type load cell is significant
parameter
From above graph it is clearly observed that as sensitivity goes
on decreasing with increasing in dimension of thickness and
height where as it increases with full length of ‘S’ type load cell.
30
31. Multi objective Optimization (… continued)
Parameter DOF Seq SS Adj SS Adj MS F P % Contribution
Thickness 2 0.002 0.002 0.001 0.50 0.668 3.440
Length 2 0.017 0.017 0.008 4.27 0.190 29.310
Height 2 0.035 0.035 0.017 8.69 0.103 60.345
Error 2 0.004 0.004 0.002 - - 6.897
Total 8 0.058 - - - - -
ANOVA of ‘S’ type load cell for sensitivity
% contribution shows that height of ‘S’ type load cell is significant parameter
31
35. Multi objective Optimization (… continued)
Expt.
No.
Volume Sensitivity
Grade
Value
Rank
1 0.333 0.782 0.558 7
2 0.493 0.749 0.621 5
3 1.000 0.612 0.806 1
4 0.401 0.443 0.422 8
5 0.659 0.598 0.629 4
6 0.532 1.000 0.766 2
7 0.478 0.333 0.406 9
8 0.459 0.780 0.619 6
9 0.778 0.719 0.749 3
GRC and GRG values of ‘S’ type load cell
5. Determination Grey Relation Grade (GRG)
𝛾 𝑥0, 𝑥𝑖 =
1
𝑚
𝑖=1
𝑚
)
𝛾(𝑥0 𝑘 , 𝑥𝑖(𝑘)
=
0.333+0.782
2
= 0.558
For exp. no.1 of volume
A1B3C3
• Prediction of GRG under optimum Parameters
“A1B3C1”
Level Thickness Length Height
1 0.662 0.462 0.648
2 0.605 0.623 0.597
3 0.591 0.774 0.614
Max 0.662 0.774 0.648
Min 0.591 0.462 0.597
Delta 0.071 0.312 0.051
Rank 2 1 3
Total Mean of GRG 0.619
Grade Value 0.846
6. Response Table for Grey Relation Grade (GRG)
ηopt.= 0.619+(0.662-0.619)+(0.774-0.619)+(0.648-0.619)
=0.846
35
36. Multi objective Optimization (… continued)
• ‘Washer’ type load cell
1. Effect of parameters on volume
Volume analysis of ‘Washer’ type load cell
Expt.
No.
Volume (mm3)
Average
Volume
S/N Ratio
(Smaller is
better)
Mean
1 2 3
1 121611.05 121611.05 121611.05 121611.05 -101.699 121611.05
2 193993.34 193993.34 193993.34 193993.34 -105.756 193993.34
3 282460.59 282460.59 282460.59 282460.59 -109.019 282460.59
4 152013.81 152013.81 152013.81 152013.81 -103.638 152013.81
5 242491.68 242491.68 242491.68 242491.68 -107.694 242491.68
6 353075.74 353075.74 353075.74 353075.74 -110.957 353075.74
7 182416.58 182416.58 182416.58 182416.58 -105.221 182416.58
8 290990.02 290990.02 290990.02 290990.02 -109.278 290990.02
9 423690.89 423690.89 423690.89 423690.89 -112.541 423690.89
S/N ratio calculated as per
above mentioned procedure
36
37. Multi objective Optimization (… continued)
Mean volume response table of ‘Washer’ type load cell
Levels Height
(mm)
Outer Diameter
(mm)
1 199354.993 152013.813
2 249193.743 242491.680
3 299032.497 353075.740
Max 299032.497 353075.740
Min 199357.993 152013.813
Delta 99677.503 201061.927
Rank 2 1 60
50
40
350000
300000
250000
200000
150000
96
80
64
Height
Mean
of
Means
Outer Diameter
Main Effects Plot for Means
Data Means
Effects of process parameters on sensitivity
Rank shows that outer diameter of ‘Washer’ type
load cell is significant parameter From above graph it is clearly observed that as volume goes on
increasing with increasing in dimension of height and outer
diameter of ‘Washer’ type load cell.
37
38. Multi objective Optimization (… continued)
ANOVA of ‘Washer’ type load cell for volume
Parameter DOF Seq. SS Adj. SS Adj. MS F P % C
Height 2 14903407006 14903407006 7451703503 18.37 0.01 19.263
OD 2 60840977038 60840977038 30420488519 75 0.001 78.640
Error 4 1622426018 1622426018 405606504 - - 2.097
Total 8 77366810061 - - - - 100.000
% contribution shows that Outer Diameter of ‘Washer’ type load cell is significant
parameter
38
39. Multi objective Optimization (… continued)
Expt.
No.
Sensitivity (µstrain/N X 10-3)
Average
sensitivity
S/N Ratio
(Larger is
better)
Mean
(10-3)
1 2 3
1 1.57 1.57 1.57 1.57 -56.082 1.57
2 0.97 0.97 0.97 0.97 -60.265 0.97
3 0.6 0.6 0.6 0.6 -63.609 0.6
4 1.59 1.59 1.59 1.59 -55.972 1.59
5 0.98 0.98 0.98 0.98 -60.175 0.98
6 0.67 0.67 0.67 0.67 -63.479 0.67
7 1.62 1.62 1.62 1.62 -55.810 1.62
8 1 1 1 1 -60.000 1
9 0.68 0.68 0.68 0.68 -63.350 0.68
Sensitivity analysis of ‘Washer’ type load cell
2. Effect of parameters on sensitivity
S/N ratio calculated as per above
mentioned procedure
39
40. Multi objective Optimization (… continued)
Levels Height
(mm)
Outer Diameter
(mm)
1 0.0011 0.0016
2 0.0011 0.0010
3 0.0011 0.0007
Max 0.0011 0.0016
Min 0.0011 0.0007
Delta 0 0.0009
Rank 2 1
Mean sensitivity response table of ‘Washer’ type load cell
60
50
40
0.0016
0.0014
0.0012
0.0010
0.0008
0.0006
96
80
64
Height
Mean
of
Means
Outer Diameter
Main Effects Plot for Means
Data Means
Effects of process parameters on sensitivity
Rank shows that outer diameter of ‘Washer’ type load
cell is significant parameter
From above graph it is clearly observed that as sensitivity goes
on increasing with increasing in dimension of height and
decrease for outer diameter of ‘Washer’ type load cell.
40
41. Multi objective Optimization (… continued)
Parameter DOF Seq. SS Adj. SS Adj. MS F P % C
Height 2 0.00000 0.00000 0.00000 13.82 0.016 0
OD 2 0.0000013 0.0000013 0.0000007 10823.09 0 100
Error 4 0.00000 0.00000 0.00000 - - 0
Total 8 0.0000013 - - - - 100
ANOVA of ‘Washer’ type load cell for sensitivity
% contribution shows that Outer Diameter of ‘Washer’ type load cell is significant
parameter
41
42. Multi objective Optimization (… continued)
Expt.
No.
Volume Sensitivity
Grade
Value
Rank
1 0.333 0.935 0.634 4
2 0.444 0.467 0.455 9
3 0.606 0.333 0.470 8
4 0.378 0.960 0.669 3
5 0.528 0.472 0.500 7
6 0.774 0.337 0.556 5
7 0.425 1.000 0.713 1
8 0.624 0.482 0.553 6
9 1.000 0.341 0.670 2
GRC and GRG values of ‘Washer’ type load cell
Level Height Outer Diameter
1 0.52 0.672
2 0.575 0.503
3 0.645 0.565
Max 0.645 0.672
Min 0.52 0.503
Delta 0.125 0.169
Rank 2 1
Total Mean of GRG 0.580
Grade Value 0.734
• Prediction of GRG under optimum Parameters
“A3B1”
A3B1
Response Table for Grey Relation Grade (GRG)
42
43. Multi objective Optimization (… continued)
• ‘Double Ended Shear Beam’ type load cell (DESB)
1. Effect of parameters on volume
Volume analysis of ‘DESB’ type load cell
S/N ratio calculated as per
above mentioned procedure
Expt.
No.
Volume (mm3)
Average
Volume
S/N Ratio
(Smaller is
better)
Mean
1 2 3
1 105626.342 105626.342 105626.342 105626.342 -100.475 105626.342
2 224775.931 224775.931 224775.931 224775.931 -107.035 224775.931
3 388948.825 388948.825 388948.825 388948.825 -111.798 388948.825
4 213486.677 213486.677 213486.677 213486.677 -106.587 213486.677
5 412802.109 412802.109 412802.109 412802.109 -112.315 412802.109
6 256762.306 256762.306 256762.306 256762.306 -108.191 256762.306
7 358130.032 358130.032 358130.032 358130.032 -111.081 358130.032
8 239508.947 239508.947 239508.947 239508.947 -107.586 239508.947
9 515508.588 515508.588 515508.588 515508.588 -114.245 515508.588 43
44. Multi objective Optimization (… continued)
Mean volume response table of ‘DESB’ type load cell
Effects of process parameters on sensitivity
Rank shows that thickness of ‘DESB’ type load cell is
significant parameter From above graph it is clearly observed that as volume goes on
increasing with increasing in dimension of length, height and
thickness of ‘DESB’ type load cell.
Levels Length
(mm)
Height
(mm)
Thickness
(mm)
1 239783.699 225747.684 200632.532
2 294350.364 292362.329 317923.732
3 371049.189 387073.240 386626.989
Max 371049.189 387073.240 386626.989
Min 239783.699 225747.684 200632.532
Delta 131265.490 161325.556 185994.457
Rank 3 2 1
256.1
197.0
137.9
400000
350000
300000
250000
200000
65
50
35
55.9
43.0
30.1
400000
350000
300000
250000
200000
Length
Mean
of
Means
Height
Thickness
Main Effects Plot for Means
Data Means
44
45. Multi objective Optimization (… continued)
ANOVA of ‘DESB’ type load cell for volume
% contribution shows that thickness of ‘DESB’ type load cell is significant parameter
Parameters DOF Seq. SS Adj. SS Adj. MS F P % C
Length 2 26090859503 26090859503 13045429752 3.15 0.241 20.56
Height 2 39433602565 39433602565 19716801283 4.76 0.174 31.07
Thickness
2 53071301307 53071301307 26535650654 6.4 0.135 41.82
Error 2 8289632204 8289632204 4144816102 - - 6.5
Total 8 126885395579 - - - - 100
45
46. Multi objective Optimization (… continued)
Sensitivity analysis of ‘DESB’ type load cell
2. Effect of parameters on sensitivity
S/N ratio calculated as per above
mentioned procedure
Expt.
No.
Sensitivity(µstrain/N X 10-3)
Average
Volume
S/N Ratio
(Larger
is better)
Mean
(10-3)
1 2 3
1 12.40 12.40 12.40 12.40 -38.132 12.40
2 6.20 6.20 6.20 6.20 -44.152 6.20
3 3.70 3.70 3.70 3.70 -48.636 3.70
4 8.40 8.40 8.40 8.40 -41.514 8.40
5 4.80 4.80 4.80 4.80 -46.375 4.80
6 15.00 15.00 15.00 15.00 -36.478 15.00
7 6.00 6.00 6.00 6.00 -44.437 6.00
8 19.80 19.80 19.80 19.80 -34.067 19.80
9 4.50 4.50 4.50 4.50 -46.936 4.50
46
47. Multi objective Optimization (… continued)
Mean sensitivity response table of ‘DESB’ type load cell
Effects of process parameters on sensitivity
Rank shows that thickness of ‘Washer’ type load cell is
significant parameter
From above graph it is clearly observed that as sensitivity goes
on increasing with increasing in dimension of length and
decrease for height and thickness of ‘DESB’ type load cell.
Levels Length (mm) Height (mm) Thickness (mm)
1 7.433 8.933 15.733
2 9.40 10.267 6.367
3 10.10 7.733 4.833
Max 10.10 10.267 15.733
Min 7.433 7.733 4.833
Delta 2.667 2.533 10.900
Rank 3 2 1
256.1
197.0
137.9
0.0150
0.0125
0.0100
0.0075
0.0050
65
50
35
55.9
43.0
30.1
0.0150
0.0125
0.0100
0.0075
0.0050
Length
Mean
of
Means
Height
Thickness
Main Effects Plot for Means
Data Means
47
48. Multi objective Optimization (… continued)
ANOVA of ‘DESB’ type load cell for sensitivity
% contribution shows that thickness of ‘Washer’ type load cell is significant
parameter
Parameter DOF Seq. SS Adj. SS Adj. MS F P %C
Length 2 0.0000115 0.0000115 0.00000575 0.66 0.602 4.64
Height 2 0.0000096 0.0000096 0.0000048 0.55 0.643 3.88
Thickness 2 0.0002089 0.0002089 0.00010445 12.02 0.077 84.43
Error 2 0.0000174 0.0000174 0.0000087 - - 7.03
Total 8 0.0002474 - - - - 100
48
49. Multi objective Optimization (… continued)
GRC and GRG values of ‘DESB’ type load cell
• Prediction of GRG under optimum Parameters
“A3B3C1”
A3B2C1
Level Length Height Thickness
1 0.492 0.507 0.628
2 0.567 0.595 0.540
3 0.661 0.619 0.554
Max 0.661 0.619 0.628
Min 0.492 0.507 0.540
Delta 0.169 0.113 0.088
Rank 1 2 3
Total Mean of GRG 0.5737
Grade Value 0.7611
Response Table for Grey Relation Grade (GRG)
Expt.
No.
Volume Sensitivity
Grade
Value
Rank
1 0.333 0.642 0.488 7
2 0.488 0.419 0.454 9
3 0.738 0.333 0.536 6
4 0.473 0.494 0.484 8
5 0.781 0.372 0.576 4
6 0.532 0.751 0.642 3
7 0.685 0.413 0.549 5
8 0.508 1.000 0.754 1
9 1.000 0.361 0.681 2
49
50. Multi objective Optimization (… continued)
Sr.
No
.
Initial
Setting
Predicted
Value
FEA
Validation
1 Optimal
parameters
A1B3C3 A1B3C1 A1B3C1
2 Grey Relational
Grade
0.806 0.844 0.810
Sr.
No
.
Initial
Setting
Predicted
Value
FEA
Validation
1 Optimal
parameters
A3B1 A3B1 A3B1
2 Grey Relational
Grade
0.713 0.734 0.713
Sr.
No
.
Initial
Setting
Predicted
Value
FEA
Validation
1 Optimal
parameters
A3B2C1 A3B3C1 A3B3C1
2 Grey Relational
Grade
0.754 0.7611 0.808
Outcomes for Multi-Objective optimization ‘S’ load
cell
Outcomes for Multi-Objective optimization ‘Washer’
load cell
Outcomes for Multi-Objective optimization ‘Double
Ended Shear Beam’ load cell
50
51. Validation using Photoelastic Model (ESA)
• Photoelasticity
Plane polariscope
• Material
Epoxy resin (Araldite CY-230 and Hardener HY-951)
Circular polariscope
51
53. Validation using Photoelastic Model (… continued)
Photoelastic model of ‘S’ type load cell Initial mounting arrangement of ‘S’ load cell on polariscope
• ‘S’ type model
53
54. Validation using Photoelastic Model (… continued)
‘S’ type load cell model in bright field under load
3kg
Load (Kg) Fringe order
3 2.33
σ1 =
N X Fσ
h
Where,
σ1 = Stress in model (N/mm2)
N = Fringe order
Fσ = Fringe value (N/mm)= 12.37 (N/mm)
h= Thickness of model (mm)
σ1 =
2.33X 12.37
10.1
=2.85 N/mm2
54
55. Validation using Photoelastic Model (… continued)
Scaling model results to prototype
Where,
σm and σp = stress in a model and prototype respectively (N/mm²)
hm and hp = thickness of model and prototype respectively (mm)
lm and lp = linear dimension of model and prototype respectively (mm)
σP = 2.85𝑋
2425.5
97.81
𝑋
10.1
12.5
𝑋 2
= 115.47 N/mm²
55
56. Validation using Photoelastic Model (… continued)
• ‘Washer’ type model
Photoelastic model of ‘Washer’
type load cell
Initial mounting arrangement of ‘Washer’ load cell on polariscope
56
57. Validation using Photoelastic Model (… continued)
‘Washer’ type load cell model in bright field
under load 6kg
‘Washer’ type load cell model in dark
field under load 6kg
Load (Kg) Fringe order
6 0.60
σ1 =
N X Fσ
h
= 1.482 N/mm²
σP = m 𝑋
Pp
Pm
𝑋
hm
hp
𝑋
lm
𝑙𝑝
= 90.73 N/mm²
57
58. Validation using Photoelastic Model (… continued)
• ‘Double Ended Shear Beam’ type model
Photoelastic model of ‘Double Ended Shear
Beam’ type load cell
Initial mounting arrangement of ‘Double Ended Shear Beam’
load cell on polariscope
58
59. Validation using Photoelastic Model (… continued)
‘Double Ended Shear Beam’ type load cell model in bright
field under load 8 kg
‘Double Ended Shear Beam’ type load cell model in
dark field under load 8 kg
59
60. Validation using Photoelastic Model (… continued)
Enlarged View nearer to point of interest
Load (Kg) Fringe order
8 1.62
σ1 =
N X Fσ
h
= 4N/mm2
σP = σm 𝑋
Pp
Pm
𝑋
hm
hp
𝑋
lm
𝑙𝑝
= 176.40 N/mm2
60
61. Validation using Photoelastic Model (… continued)
Comparison between Ansys and Photoelasticity results
Sr.
No.
Type of load
cell
p N/mm2 a N/mm2
1 S 115.47 122.3
2 Washer 90.73 97.36
3
Double Ended
Shear Beam 176.40 185.30
Where,
p =Stress in photoelastic model N/mm2
a =Ansys stress N/mm2
61
62. Conclusions
1. ‘S’ type load cell, used for research work having volume 27495.9 mm3 and sensitivity 0.130µstrain/N. To get the
optimum solution FEM and Taguchi with GRA method was used which gives volume 26921.02 mm3and sensitivity
0.283 µstrain/N i.e. volume is reduced by 2.08% and sensitivity is increased by 54.06%.
2. ‘Washer’ type load cell, used for research work having volume 242491.68 mm3 and sensitivity 0.98 X 10-3µstrain/N.
The Optimization method gives results as volume 182416.58 mm3and sensitivity 1.62 X 10-3µstrain/N i.e. volume is
reduced by 24.77% and sensitivity is increased by 39.50%.
3. ‘Double Ended Shear Beam’ type load cell, used for research work volume 315960 mm3 and sensitivity 5.88X 10-3
µstrain/N. The Optimization method gives results as volume 314210 mm3and sensitivity 19.85 X 10-3 µstrain/N i.e.
volume is reduced by 0.55% and sensitivity is increased by 70.37%.
The following conclusions are drawn from the study,
62
64. References
1. Antony J. J. and Antony F., (2001) “Teaching the Taguchi method to industrial engineers,” Work Study 50, 4, 141-149.
2. Baadkar C., (2010) “Semi-Trailer Structural Failure Analysis Using Finite Element Method,” A Thesis Submitted in Partial Fulfilment
of the Requirements for the Degree of Master of Engineering in Mechanical Engineering in the University of Canterbury.
3. Bethe K.,(1994) “Optimization of a compact force-sensor/load-cell family,” Proceedings of Euro-sensors, VIII, 42, 1-3, 362-367.
4. Dabade U., (2013) “Multi-objective Process Optimization to Improve Surface Integrity on Turned Surface of Al/SiCp Metal Matrix
Composites Using Grey Relational Analysis,” Procedia CIRP, 7, 299-304.
5. Daily J., Riley W.,(1991) “Experimental Stress Analysis,” Tata McGraw Hill International, 3rdEdition, 476-477.
6. Dange M., Zaveri S., Khamankar S., (2014) “Stress Analysis of Bell Crank Lever,” International Journal on Recent and Innovation
Trends in Computing and Communication, 2, 8, 2423-2430.
7. Das D., Sahoo A., Das R., Routara B., (2014) “Investigations on hard turning using coated carbide insert: Grey based Taguchi and
regression methodology,” Procedia Material Science,6, 1351-1358.
8. Das M.K., Kumar K., Barman T. Kr., Sahoo P., (2014) “Optimization of Process Parameters in Plasma Arc Cutting of EN31 Steel Based
on MRR and Multiple Roughness Characteristics using Grey Relation Analysis,” Procedia Material Science, 5, 1550-1559.
9. Dean E., (1991) “Taguchi Approach to Design Optimization for Quality and Cost: an Overview,” Annual Conference of the
International Society of Parametric Analysis, 1-9.
10. Deng J., (1989) “Introduction to Grey System”, Journal of Grey System, 1, 1-24.
64
65. References (… continued)
11. Drouet J.M., Champoux Y., (2014) “Designing a Strain Gauge Transducer for Dynamic Load Measurement in Cycling Using Numerical
Simulation,” Procedia Engineering, 72,304-309.
12. Durairaj M., Sudharsun D., Swamynathan N., (2013) “Analysis of Process Parameters in Wire EDM with Stainless Steel using Single
Objective Taguchi Method and Multi Objective Grey Relational Grade,” Procedia Engineering, 64 ,868 – 877.
13. Equbal M. I.,Ohdar R.,Bhat M. N.,Lone S.A.,(2012) “Preform Shape Optimization of Connecting Rod Using Finite Element Method and
Taguchi Method,” International Journal of Modern Engineering Research 2,6, 4532-4539.
14. Ghanvat S.M., Patil G.H., (2012) “Shape Optimization of ‘S’ Type Load Cell Using Finite Element Method,” International Journal of
Engineering Innovation and Research, 1, 3, 310-316.
15. Hernandz W., (2006) “Improving the Response of a Load Cell by Using Optimal Filtering,” Sensors, 6, 697-711.
16. Kacker R., Lagergren E. and Filliben J., (1991) “Taguchi Vs Orthogonal Arrays Are Classical Designs of Experiments,” Journal of Research
of the National Institute of Standards and Technology 96, 5, 577-591
17. Kamaruddin S., Khan Z. and Wan K., (2004) “The use of the Taguchi method in determining the optimum plastic injection molding
parameters for the production of a consumer product,” Jurnal Mekanikal18, 98-110.
18. Kamble V. A., Gore P.N., (2012) “Use of FEM and Photo Elasticity for Shape Optimization of ‘S’ Type Load Cell,” Indian Journal of Science
and Technology, 5, 3, 2384-2389.
19. Khot M., Gawali B., (2014) “Finite Element Analysis and Optimization of Flexure Bearing for Linear Motor Compressor,” Physics Procedia,
67, 379-385.
65
66. References (… continued)
20. Kostka J., Frankovsky P., Pastor M., Trebuna F., Simcak F., (2014) “Application of Photostress Method in Stress Analysis of Structural
Elements Under Consideration of Centrifugal Force Effect,” Procedia Engineering, 96, 235-241.
21. Kuhnel M., Hilbrunner F., Buchner H., Jager G., Manske E., Frohlich T.,(2014)“Traceable Measurement of Mechanical Parameters of Double
Bending Beam Force Transducers According to EN ISO 376” Measurment,51,336-342.
22. Pardhi D., Khamankar S., (2014) “Stress Analysis of Spline Shaft Using Finite Element Method and its Experimental Verification by Photo
Elasticity,” Internationl Journal of Mechanical Engineering and Robotics Research, 3, 4, 451-458.
23. Patil G., Inamdar K., (2014) “Optimization of Casting Process Parameters using Taguchi Method,” International Journal of Engineering
Development and Research 2, 2, 2506-2511.
24. Patil R.D., Kumar B., (2013) “An Approach to Finite Elements Analysis of Boiler Tube-Sheet,” American Journal of Engineering Research, 2,
8, 8-11.
25. Phadke M.S., (2012) “Quality engineering using robust design”, Pearson education, South Asia.
26. Pinit P., (2009) “Development of Window-Based Program for Analysis and Visualization of Two-Dimensional Stress Field in Digital Photo
Elasticity,” Songlanakar in Journal of Science and Technology, 31, 2, 205-212.
27. Raghuraman S., Thiruppathi K., Panneerselvam T., Santosh S., (2013) “Optimization of EDM Parameters using Taguchi Method and Grey
Relation Analysis for Mild Steel IS 2026,” International Journal of Innovative Research in Science, Engineering and Technilogy,2,7,3095-3104.
28. Ragulskis M., Sanjuan M.A.F., (2008) “Chaotic Pattern of Unsmoothed Isochromatics around the Regions of Concentrated Stresses,”
Computer and Graphics, 32, 116-119. 66
67. References (… continued)
29. Rao R.,“Advanced Modeling and optimization of Manufacturing Processes,” International Research and Development Springer,2-4.
30. Rao S., “Engineering Optimization Theory and Practice, ” John Wiley and Sons, 4th Edition.
31. Rao V. (2011) “Advanced Modeling and Optimization of Manufacturing Processes” International Research and Development 1, 1-380.
32. Reddy P., Jagadeeshgouda K., Malagi S., (2015) “Analysis of Stress Distribution in a Curved Structure Using Photoelastic and Finite Element
Method,” IOSR Journal of Mechanical and Civil Engineering, 12, 1, 112-116.
33. Rice L.,(2010)“Load Cell and Weigh Module Hanbook,” Rice Lake Weighing System, 1-3.
34. Roman P., Franc N., (2010) “A High-Sensitivity Hydraulic Load Cell for Small Kitchen Appliances,” Sensors ISSN, 10, 8, 8452-8465.
35. Sadhu Singh, (1996) “Experimental Stress Analysis,” Khanna Publishers, 281,357.
36. Saedon J.B., Jaafar N., Tahaya M.A., Saad N., Kasim M.S., (2014) “Multi-objective optimization of titanium alloy through orthogonal array
and grey relation analysis in WEDM,” Procedia Technology, 15,833-841.
37. Schoonover R.M., (1979) “A High Precision Load Cell Mass Comparator,” Journal of research of the National Bureau of Standards, 84, 5,347-
351.
38. Shete H., Pasale R., Eitawade E., (2012) “Photoelastic Stress Analysis & Finite Element Analysis of an Internal Combustion Engine Piston,”
International Journal of Scientific & Engineering Research, 3, 7, 1-5.
39. Shetty P.P., Patil N.K., Meshramkar R., Nadiger R.K., (2013) “A Fast and Economical Photoelastic Model of the Teeth and Surrounding
Structure,” ISOR Journal of Dental and Medical Science, 3, 5, 28-33.
40. Shivade A., Shinde V., (2014) “Multi-objective optimization in WEDM of D3 tool steel using integrated approach of Taguchi method & Grey
relational analysis,” J Ind Eng Int,149–162.
67
68. References (… continued)
41. Sifeng L. and Yi L., (2006) “Grey Information Theory and Practical Applications”, Springer-Verlag, London
42. Sinna A.A., Park Y.K., Kang D.I., Kim M.S., (2010)“Optimum Design of DMW Loading Frame,” IMCKO 2010 TC3, TC5 and TC22
Conference Metrology in Modern Context, 337-341.
43. Sinna A.A., Park Y.Y., Kang D.I., Kim M.S., (2009) “The Influence of Loading Frame Stiffness on Load Cell-Deadweight Force Machine
Interaction,” Measurement, 42,830-835.
44. Soni A., Priyadarshni P., “Finite Element Analysis and Optimization of a Beam Type Load Cell for an External Balance Design,” Page no.1-12.
45. Steve P., (2010) “Load Cell Application and Test Guideline,” Scale Manufacturers Association.
46. Talla G., Gangopadhyay S., Biswas C., (2014) “Multi Response Optimization of Powder Mixed Electric Discharge Machining of
Aluminum/Alumina Metal Composite Using Grey Relation Analysis,” Procedia Material Science, 5, 1633-1639.
47. Thakkar K.H., Prajapati V.M., Patel B.D., (2013)“Performance Evaluation of Strain Gauge Based Load Cell to Improve Weighing Accuracy,”
International Journal of Latest Trends in Engineering and Technology, 2, 1, 103-107.
48. Thein C.K., (2013) “Structural Sizing and Shape Optimization of a Load Cell,” International Journal of Engineering and Technology, 2, 7,196-
201.
49. Uddanwadiker R., (2011) “Stress Analysis of Crane Hook and Validation by Photo-Elasticity”, Scientific Research, 3, 936-941.
50. Unal R. and Dean E. (1991) “Taguchi approach to design optimization for quality and cost: an overview” Annual Conference of the
International Society of Parametric Analysts 1, 1-10.
51. ADI ARTECH Transducers Pvt. Ltd. Cat. No. 20210-06 http://www.adiartech.com/productdetail.aspx?pid=59&cid=1. 68
